Skill UI

Generates production-ready Claude-powered agents with Google’s ADK, bundling React-style loops, tool-augmented capabilities, multi-agent orchestration, workflow automation, and test scaffolding.

Deploys and orchestrates Vertex AI ADK agents via the A2A protocol, covering AgentCard discovery, sandboxed code execution, memory banks, orchestration patterns, and Vertex AI Agent Engine rollout for CI/CD-style automation.

Provides best-practice guidance for building production-ready ADK agents with modular code, structured tools, regression tests, and deployment automation across Python, Java, or Go runtimes.

Automatically loads AGENTS.md alongside CLAUDE.md whenever Claude Code starts, changes directories, or users ask for agent context, ensuring project-specific agent instructions take effect before other skills run.

A comprehensive toolkit for designing, architecting, and evaluating complex multi-agent systems. It provides structured patterns—including Supervisor, Swarm, and Pipeline—for system architecture, defines agent roles (Coordinator, Specialist), and covers best practices for communication (Event Sourcing, Message Passing), tool design, and safety guardrails, ensuring robust and scalable AI workflows.

Guides designers in creating Claude Code agents by defining frontmatter, triggering conditions, system prompt structure, color/tool choices, and quality best practices for autonomous workflows.

A comprehensive framework for rigorously testing and benchmarking LLM agents. It moves beyond simple pass/fail checks to assess complex behaviors, reliability metrics, and capability consistency across multiple runs. Ideal for production monitoring and identifying subtle failure modes in advanced AI agents.

Build persistent agents on Azure AI Foundry through the Microsoft Agent Framework Python SDK, hooking into AzureAIAgentsProvider for tools, hosted services, streaming, and threaded conversations.

Automates Agent Mail via Composio's Rube MCP toolkit, guiding users to discover current tool schemas, verify connections, and execute workflows safely.

Orchestrates multiple local CLI agents via tmux sessions, providing start/stop/monitor controls, task assignment, and cron-friendly scheduling for parallel workflows and log tracking.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.